The Algorithm of Automatic Text Summarization Based on Network Representation Learning

  • Xinghao Song
  • Chunming YangEmail author
  • Hui Zhang
  • Xujian Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


The graph models are an important method in automatic text summarization. However, there will be problems of vector sparseness and information redundancy in text map to graph. In this paper, we propose a graph clustering summarization algorithm based on network representation learning. The sentences graph was construed by TF-IDF, and controlled the number of edges by a threshold. The Node2Vec is used to embedding the graph, and the sentences were clustered by k-means. Finally, the Modularity is used to control the number of clusters, and generating a brief summary of the document. The experiments on the MultiLing 2013 show the proposed algorithm improves the F-Score in ROUGE-1 and ROUGE-2.


Text summarization Network representation learning Graph clustering Modularity 



This work is supported by the Ministry of education of Humanities and Social Science project (17YJCZH260), the Next Generation Internet Technology Innovation Project (NGII20170901), the Fund of Fundamental Sichuan Civil-military Integration (JMRHH01, 18sxb017, 18sxb028).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xinghao Song
    • 1
  • Chunming Yang
    • 1
    • 3
    Email author
  • Hui Zhang
    • 2
  • Xujian Zhao
    • 1
  1. 1.School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyangChina
  2. 2.School of ScienceSouthwest University of Science and TechnologyMianyangChina
  3. 3.Sichuan Civil-Military Integration InstituteMianyangChina

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